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104 lines
2.7 KiB
C++
104 lines
2.7 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
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#include <stdio.h>
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#include <unsupported/Eigen/NumericalDiff>
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#include "main.h"
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// Generic functor
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template <typename _Scalar, int NX = Dynamic, int NY = Dynamic>
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struct Functor {
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typedef _Scalar Scalar;
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enum { InputsAtCompileTime = NX, ValuesAtCompileTime = NY };
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typedef Matrix<Scalar, InputsAtCompileTime, 1> InputType;
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typedef Matrix<Scalar, ValuesAtCompileTime, 1> ValueType;
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typedef Matrix<Scalar, ValuesAtCompileTime, InputsAtCompileTime> JacobianType;
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int m_inputs, m_values;
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Functor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
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Functor(int inputs_, int values_) : m_inputs(inputs_), m_values(values_) {}
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int inputs() const { return m_inputs; }
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int values() const { return m_values; }
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};
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struct my_functor : Functor<double> {
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my_functor(void) : Functor<double>(3, 15) {}
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int operator()(const VectorXd &x, VectorXd &fvec) const {
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double tmp1, tmp2, tmp3;
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double y[15] = {1.4e-1, 1.8e-1, 2.2e-1, 2.5e-1, 2.9e-1,
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3.2e-1, 3.5e-1, 3.9e-1, 3.7e-1, 5.8e-1,
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7.3e-1, 9.6e-1, 1.34, 2.1, 4.39};
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for (int i = 0; i < values(); i++) {
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tmp1 = i + 1;
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tmp2 = 16 - i - 1;
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tmp3 = (i >= 8) ? tmp2 : tmp1;
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fvec[i] = y[i] - (x[0] + tmp1 / (x[1] * tmp2 + x[2] * tmp3));
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}
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return 0;
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}
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int actual_df(const VectorXd &x, MatrixXd &fjac) const {
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double tmp1, tmp2, tmp3, tmp4;
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for (int i = 0; i < values(); i++) {
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tmp1 = i + 1;
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tmp2 = 16 - i - 1;
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tmp3 = (i >= 8) ? tmp2 : tmp1;
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tmp4 = (x[1] * tmp2 + x[2] * tmp3);
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tmp4 = tmp4 * tmp4;
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fjac(i, 0) = -1;
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fjac(i, 1) = tmp1 * tmp2 / tmp4;
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fjac(i, 2) = tmp1 * tmp3 / tmp4;
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}
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return 0;
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}
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};
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void test_forward() {
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VectorXd x(3);
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MatrixXd jac(15, 3);
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MatrixXd actual_jac(15, 3);
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my_functor functor;
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x << 0.082, 1.13, 2.35;
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// real one
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functor.actual_df(x, actual_jac);
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// std::cout << actual_jac << std::endl << std::endl;
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// using NumericalDiff
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NumericalDiff<my_functor> numDiff(functor);
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numDiff.df(x, jac);
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// std::cout << jac << std::endl;
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VERIFY_IS_APPROX(jac, actual_jac);
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}
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void test_central() {
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VectorXd x(3);
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MatrixXd jac(15, 3);
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MatrixXd actual_jac(15, 3);
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my_functor functor;
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x << 0.082, 1.13, 2.35;
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// real one
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functor.actual_df(x, actual_jac);
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// using NumericalDiff
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NumericalDiff<my_functor, Central> numDiff(functor);
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numDiff.df(x, jac);
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VERIFY_IS_APPROX(jac, actual_jac);
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}
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EIGEN_DECLARE_TEST(NumericalDiff) {
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CALL_SUBTEST(test_forward());
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CALL_SUBTEST(test_central());
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}
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